Cognitive Algorithms and digitized Tissue – based Diagnosis

Aims: To analyze the nature and impact of cognitive algorithms and programming on digitized tissue – based diagnosis. Definitions: Digitized tissue – based diagnosis includes all computerized tissue investigations that contribute to the most appropriate description and forecast of the actual patient’s disease [1]. Cognitive algorithms are programs that encompass machine learning, reasoning, and human – computer interaction [2]. Theoretical considerations: Digitized blood data, objective clinical findings, microscopic, gross, radiological images and gene alterations are analyzed by specialized image analysis methods, and transferred in numbers and vectors. These are analyzed by statistical procedures. They include higher order statistics such as multivariate analysis, neural networks and ‘black box’ strategies, for example ‘deep learning’ or ‘Watson’ approaches. These algorithms can be applied at different cognitive ‘levels’, to reach a digital decision for different procedures which should assist the patient’s health condition. These levels can be grouped in self learning, self promoting, self targeting, and self exploring algorithms. Each of them requires a memory and neighbourhood condition. Self targeting and exploring algorithms are circumscribed mechanisms with singularities and repair procedures. They develop self recognition.   Consecutives: Medical doctors including pathologists are commonly not trained to understand the basic principles and workflow of applied or potential future procedures. At present, basic medical data only serve for simple cognitive algorithms. Most of the investigations focus on ‘deep learning’ procedures. The applied learning and decision algorithms might be modified and themselves be used for ‘next order cognitive algorithms’. Such systems will develop their own strategies, and become independent from potential human interactions. The basic strategy of such IT systems is described herein. Perspectives: Medical doctors including pathologists should be aware about the abilities to enhance their work by supporting tools. In some case the users may not be able to fully understand these tools. Furthermore, these tools will probably become self learning, and, therefore, seem to propose the daily workflow probably without any medical control or even interaction

Mining visual collocation patterns via self-supervised subspace learning

Traditional text data mining techniques are not directly applicable to image data which contain spatial information and are characterized by high-dimensional visual features. It is not a trivial task to discover meaningful visual patterns from images because the content variations and spatial dependence in visual data greatly challenge most existing data mining methods. This paper presents a novel approach to coping with these difficulties for mining visual collocation patterns. Specifically, the novelty of this work lies in the following new contributions: 1) a principled solution to the discovery of visual collocation patterns based on frequent itemset mining and 2) a self-supervised subspace learning method to refine the visual codebook by feeding back discovered patterns via subspace learning. The experimental results show that our method can discover semantically meaningful patterns efficiently and effectively.Accepted versio